CN116721731A - Medical record fitting algorithm for meridian doctor - Google Patents
Medical record fitting algorithm for meridian doctor Download PDFInfo
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- CN116721731A CN116721731A CN202310730893.4A CN202310730893A CN116721731A CN 116721731 A CN116721731 A CN 116721731A CN 202310730893 A CN202310730893 A CN 202310730893A CN 116721731 A CN116721731 A CN 116721731A
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- 238000000034 method Methods 0.000 claims abstract description 27
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 238000013500 data storage Methods 0.000 claims description 9
- 230000009467 reduction Effects 0.000 claims description 9
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 238000000638 solvent extraction Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007721 medicinal effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The application discloses a medical record fitting algorithm for a meridian doctor, which comprises the following steps of: A. firstly, receiving electronic medical record data and preprocessing the electronic medical record data; B. classifying the preprocessed electronic medical record data to obtain classified medical record data; C. fitting the classified medical record data; D. finally, the fitted electronic medical record data is encrypted and stored, and the method can be used for sorting, integrating and encrypting and storing the electronic medical record data, so that the processing efficiency of the electronic medical record data is improved, the subsequent flow efficiency optimization construction can be conveniently carried out, the operation cost of a hospital is reduced, and the working intensity of medical staff is lightened; the data classification method can improve the identification accuracy of the data, further improve the classification accuracy of the data and avoid classification errors.
Description
Technical Field
The application relates to the technical field of medical record fitting, in particular to a medical record fitting algorithm for a meridian doctor.
Background
Medical records are records of medical activities such as examination, diagnosis and treatment of occurrence, development and prognosis of diseases of patients by medical staff. The collected data is also summarized, arranged and comprehensively analyzed, and the medical health file of the patient is written according to a specified format and requirements. The medical records are not only summaries of clinical practice work, but also legal basis for exploring disease rules and handling medical disputes, and are valuable wealth of the country. The medical record has important effects on medical treatment, prevention, teaching, scientific research, hospital management and the like.
Fitting and sorting data are common means for data processing, but in the prior art, in the process of fitting electronic medical record data, complicated processing is usually required, so that the fitting efficiency is low, and therefore, improvement is needed.
The information disclosed in this background section is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a medical record fitting algorithm for a medical manager to solve the problems in the background technology.
In order to achieve the above purpose, the present application provides the following technical solutions: a medical history fitting algorithm for a medical manager, comprising the steps of:
A. firstly, receiving electronic medical record data and preprocessing the electronic medical record data;
B. classifying the preprocessed electronic medical record data to obtain classified medical record data;
C. fitting the classified medical record data;
D. and finally, encrypting and storing the fitted electronic medical record data.
Preferably, the medical record fitting algorithm for the medical practitioners of the channels provided by the application, wherein the data preprocessing method in the step A is as follows:
a. acquiring electronic medical record data to be denoised;
b. acquiring a first model, wherein the first model is a trained neural network model;
c. acquiring noise reduction parameters of the electronic medical record data to be noise reduced based on the first model;
d. and carrying out noise reduction processing on the electronic medical record data to be noise reduced based on the noise reduction parameters.
Preferably, the medical record fitting algorithm for the medical practitioners in the channels provided by the application, wherein the data classification method in the step B is as follows:
a. setting characteristic points, and classifying the set characteristic points to obtain characteristic point classification results;
b. preprocessing data, identifying the preprocessed data, and judging set characteristic points contained in the preprocessed data;
c. and distinguishing the category of the set characteristic points contained in the preprocessed data according to the characteristic point classification result, and classifying the data according to the distinguishing result to obtain a data classification result.
Preferably, the medical record fitting algorithm for the medical practitioners of the channels provided by the application, wherein the data fitting method in the step C is as follows:
1) Obtaining classified electronic medical record data to be fitted, and carrying out linear judgment on the fitting data to obtain a corresponding judgment result;
2) Generating corresponding linear fitting data according to the judging result;
3) Extracting coordinate points of the linear fitting data, and matching the most suitable fitting function from a preset fitting function database according to the coordinate points;
4) And carrying out fitting operation on the linear fitting data according to the fitting function to realize fitting of the electronic medical record data.
Preferably, the medical record fitting algorithm for the medical manager provided by the application, wherein the data encryption storage in the step D comprises data storage and data encryption, and the data storage method comprises the following steps:
a. firstly, dividing a data storage area into a plurality of sector storage areas;
b. identifying each sector storage area;
c. carrying out Hash operation on data to be stored to obtain a data Hash value;
d. encrypting the calculated data by using an encryption key, and partitioning the data;
e. and finally storing the segmented data into a sector storage area.
Preferably, the application provides a medical record fitting algorithm for a medical manager, wherein the data encryption method comprises the following steps:
a. firstly, cleaning data to be encrypted;
b. then, performing DES encryption algorithm operation on the cleaned data to obtain encrypted primary ciphertext data;
c. then carrying out hyperchaotic encryption operation on the primary ciphertext data again to obtain secondary ciphertext data;
d. and finally, performing AES encryption operation on the secondary ciphertext data to finish final encryption of the data.
Compared with the prior art, the application has the beneficial effects that:
(1) The method can be used for sorting, integrating and encrypting the electronic medical record data, improves the processing efficiency of the electronic medical record data, can facilitate the subsequent flow efficiency optimization construction, reduces the operation cost of hospitals and lightens the working intensity of medical staff. (2) The data classification method adopted by the application can improve the identification accuracy of the data, further improve the classification accuracy of the data and avoid classification errors.
(3) The encryption storage method adopted by the application can carry out segmented encryption storage on the data, and further improves the security of the electronic medical record data.
Drawings
FIG. 1 is a flow chart of a fitting algorithm of the present application;
FIG. 2 is a flow chart of a data classification method according to the present application;
FIG. 3 is a flow chart of a data fitting method of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1-3, the present application provides a technical solution: the application provides the following technical scheme: a medical history fitting algorithm for a medical manager, comprising the steps of:
A. firstly, receiving electronic medical record data and preprocessing the electronic medical record data;
B. classifying the preprocessed electronic medical record data to obtain classified medical record data;
C. fitting the classified medical record data;
D. and finally, encrypting and storing the fitted electronic medical record data.
The application provides a medical record fitting algorithm for a medical manager, wherein the data preprocessing method in the step A is as follows:
a. acquiring electronic medical record data to be denoised;
b. acquiring a first model, wherein the first model is a trained neural network model;
c. acquiring noise reduction parameters of the electronic medical record data to be noise reduced based on the first model;
d. and carrying out noise reduction processing on the electronic medical record data to be noise reduced based on the noise reduction parameters.
The application provides a medical record fitting algorithm for a medical manager, wherein the data classification method in the step B is as follows:
a. setting characteristic points, and classifying the set characteristic points to obtain characteristic point classification results;
b. preprocessing data, identifying the preprocessed data, and judging set characteristic points contained in the preprocessed data;
c. and distinguishing the category of the set characteristic points contained in the preprocessed data according to the characteristic point classification result, and classifying the data according to the distinguishing result to obtain a data classification result.
The data classification method adopted by the application can improve the identification accuracy of the data, further improve the classification accuracy of the data and avoid classification errors.
The application provides a medical record fitting algorithm for a medical manager, wherein the data fitting method in the step C is as follows:
1) Obtaining classified electronic medical record data to be fitted, and carrying out linear judgment on the fitting data to obtain a corresponding judgment result;
2) Generating corresponding linear fitting data according to the judging result;
3) Extracting coordinate points of the linear fitting data, and matching the most suitable fitting function from a preset fitting function database according to the coordinate points;
4) And carrying out fitting operation on the linear fitting data according to the fitting function to realize fitting of the electronic medical record data.
In addition, the application provides a medical record fitting algorithm for a medical manager, wherein the data encryption storage in the step D comprises data storage and data encryption, and the data storage method comprises the following steps:
a. firstly, dividing a data storage area into a plurality of sector storage areas;
b. identifying each sector storage area;
c. carrying out Hash operation on data to be stored to obtain a data Hash value;
d. encrypting the calculated data by using an encryption key, and partitioning the data;
e. and finally storing the segmented data into a sector storage area.
The data encryption method comprises the following steps:
a. firstly, cleaning data to be encrypted;
b. then, performing DES encryption algorithm operation on the cleaned data to obtain encrypted primary ciphertext data;
c. then carrying out hyperchaotic encryption operation on the primary ciphertext data again to obtain secondary ciphertext data;
d. and finally, performing AES encryption operation on the secondary ciphertext data to finish final encryption of the data.
The encryption storage method adopted by the application can carry out segmented encryption storage on the data, and further improves the security of the electronic medical record data.
In summary, the method adopted by the application can be used for sorting, integrating and encrypting the electronic medical record data, improves the processing efficiency of the electronic medical record data, can facilitate the optimized construction of the subsequent flow efficiency, reduces the operation cost of hospitals and lightens the working intensity of medical staff.
In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present application, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A medical record fitting algorithm for a meridian doctor, which is characterized in that: the method comprises the following steps:
A. firstly, receiving electronic medical record data and preprocessing the electronic medical record data;
B. classifying the preprocessed electronic medical record data to obtain classified medical record data;
C. fitting the classified medical record data;
D. and finally, encrypting and storing the fitted electronic medical record data.
2. A medical manager record fitting algorithm according to claim 1, wherein: the data preprocessing method in the step A is as follows:
a. acquiring electronic medical record data to be denoised;
b. acquiring a first model, wherein the first model is a trained neural network model;
c. acquiring noise reduction parameters of the electronic medical record data to be noise reduced based on the first model;
d. and carrying out noise reduction processing on the electronic medical record data to be noise reduced based on the noise reduction parameters.
3. A medical manager record fitting algorithm according to claim 1, wherein: the data classification method in the step B is as follows:
a. setting characteristic points, and classifying the set characteristic points to obtain characteristic point classification results;
b. preprocessing data, identifying the preprocessed data, and judging set characteristic points contained in the preprocessed data;
c. and distinguishing the category of the set characteristic points contained in the preprocessed data according to the characteristic point classification result, and classifying the data according to the distinguishing result to obtain a data classification result.
4. A medical manager record fitting algorithm according to claim 1, wherein: the data fitting method in the step C is as follows:
1) Obtaining classified electronic medical record data to be fitted, and carrying out linear judgment on the fitting data to obtain a corresponding judgment result;
2) Generating corresponding linear fitting data according to the judging result;
3) Extracting coordinate points of the linear fitting data, and matching the most suitable fitting function from a preset fitting function database according to the coordinate points;
4) And carrying out fitting operation on the linear fitting data according to the fitting function to realize fitting of the electronic medical record data.
5. A medical manager record fitting algorithm according to claim 1, wherein: the step D of data encryption storage comprises data storage and data encryption, wherein the data storage method comprises the following steps:
a. firstly, dividing a data storage area into a plurality of sector storage areas;
b. identifying each sector storage area;
c. carrying out Hash operation on data to be stored to obtain a data Hash value;
d. encrypting the calculated data by using an encryption key, and partitioning the data;
e. and finally storing the segmented data into a sector storage area.
6. A medical manager record fitting algorithm according to claim 5, wherein: the data encryption method comprises the following steps:
a. firstly, cleaning data to be encrypted;
b. then, performing DES encryption algorithm operation on the cleaned data to obtain encrypted primary ciphertext data;
c. then carrying out hyperchaotic encryption operation on the primary ciphertext data again to obtain secondary ciphertext data;
d. and finally, performing AES encryption operation on the secondary ciphertext data to finish final encryption of the data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310730893.4A CN116721731A (en) | 2023-06-20 | 2023-06-20 | Medical record fitting algorithm for meridian doctor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310730893.4A CN116721731A (en) | 2023-06-20 | 2023-06-20 | Medical record fitting algorithm for meridian doctor |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116721731A true CN116721731A (en) | 2023-09-08 |
Family
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310730893.4A Withdrawn CN116721731A (en) | 2023-06-20 | 2023-06-20 | Medical record fitting algorithm for meridian doctor |
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| CN (1) | CN116721731A (en) |
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2023
- 2023-06-20 CN CN202310730893.4A patent/CN116721731A/en not_active Withdrawn
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Application publication date: 20230908 |